Time series data analyzer, and a computer-readable recording medium recording a time series data analysis program

Abstract

A time series data analyzer includes a segment condition input section, an analysis condition input section, and an optimum condition deriving section for analyzing all segments based on the segment conditions and analysis conditions inputted in the respective input sections, under all analysis conditions by a maximum entropy method and a nonlinear least squares method. The time series data analyzer derives the optimum segment length and the optimum lag value in correspondence to selected results, and an analysis execution section executes analysis by the maximum entropy method by setting the optimum analysis conditions derived as described above. The trending of the spectrum of electroencephalogram data is used as an indicator of the state of the subject based on the findings that the spectrum of electroencephalogram data is an exponential spectrum and the gradient changes depending on the state of the subject.

Description

BACKGROUND OF THE INVENTION[0001]1. Field of the Invention[0002]The present invention relates to an analyzer of time series data such as electroencephalogram data and a computer-readable recording medium recording a time series data analysis program.[0003]2. Description of the Related Art[0004]The present inventor found that with regard to the chaotic time series following the respective nonlinear motion equations of Lorenz, Roessler and Duffing Models, all the spectra of the time series are exponential spectra, and reported the finding on a journal written in English of The Physical Society of Japan (Document 1) in 1995. The Japanese version of Document 1 is published in Document 2 in 1996.[0005]In addition, the inventor analyzed, in the Document 1, the pulse wave (blood pressure waveform) data of one beat by the same method, and found that the spectrum of the data is an exponential spectrum, indicating the relation of physiological phenomena with chaotic characteristics. The Docum...

AI Technical Summary

Benefits of technology

[0091]Therefore, as a result of the consistency assured as described above, the respective quantities characterizing the time series data such as the gradient and divisional powers of the spectrum obtained in the analysis execution section or the analysis execution step can be more reliable compared with the quantities obtained by the conventional method.

Problems solved by technology

However, presently the state of each subject cannot be identified yet by referring to the value of the gradient only.

For example, both the overall trend of the exponential spectrum of the electroencephalogram obtained under anesthesia and the overall trend of the exponential spectrum of the electroencephalogram obtained during sound sleep are sharp, and therefore it is impossible to decide whether a person is anesthetized or is soundly sleeping, by referring to the value of the gradient only.

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